In many industries, automated processes are now used for fabrication of products, monitoring operation of systems, designing systems, interacting machinery with other objects and/or the like. In such automated industrial processes, there is a broad latitude of issues that may affect the process. These issues may cause a halt and/or break down of the automated industrial process, may degrade the operation of the automated industrial process, may change the background, environment and/or the like the automated industrial process is working in and so may change how the automated industrial process works, what the automated industrial process achieves, the goal of the automated industrial process and/or the like.
One or more of the broad latitude of issues that may affect the automated industrial process may arise during the automated industrial process causing real time changes to the operation of the automated industrial process. To mitigate such issues, forward looking models of the automated industrial process may be analyzed and used to control the automated industrial process. Such models may be determined from results from prior processes, theoretically, experimentally and/or the like. Mitigation of such issues may also be achieved by obtaining data from the automated industrial process and/or the environment in which the automated industrial process occurs and retroactively identifying the existence of an issue.
Merely by way of example, in the hydrocarbon industry, the process of drilling into a hydrocarbon reservoir may be impeded by a wide variety of problems and may require monitoring/interpretation of a considerable amount of data. Accurate measurements of downhole conditions, downhole equipment properties, geological properties, rock properties, drilling equipment properties, fluid properties, surface equipment properties and/or the like may be analyzed by a drilling crew to minimize drilling risks, to make determinations as to how to optimize the drilling procedure given the data and/or to detect/predict the likelihood of a problem/decrease in drilling efficiency and/or the like.
Similarly, in hydrocarbon exploration, hydrocarbon extraction, hydrocarbon production, hydrocarbon transportation and/or the like many conditions may be sensed and data gathered to provide for optimizing and/or preventing/mitigating issues/problems concerning the exploration, production and or transportation of hydrocarbons. Hydrocarbons are essentially a lifeblood of the modern industrial society, as such vast amounts of hydrocarbons are being prospected, retrieved and transported on a daily basis. Associated with this industry are an enormous amount of sensors gathering enumerable amounts of data relevant to the exploration, production and or transportation of hydrocarbons.
To provide for safe and efficient exploration, production and or transportation of hydrocarbons this data must be processed. While computers may be used to process the data, it is often difficult to accurately process the incoming data for real-time control of the hydrocarbon processes. As such, human operators are commonly used to control the hydrocarbon processes and to make decisions on optimizing, preventing risks, identifying faults and/or the like based on interpretation of the raw/processed data. However, optimization of a hydrocarbon process and/or mitigation and detection of issues/problems by a human controller may often be degraded by fatigue, high workload, lack of experience, the difficulty in manually analyzing complex data and/or the like. Furthermore, noisy data may have a significant impact on a human observer's ability to take note of or understand the meaning occurrences reflected in the data.
The detection of occurrences reflected in the data goes beyond detection of issues and problems. Accurate analysis of operating conditions may allow for an operator to operate the industrial process at near optimal conditions. For example, in the hydrocarbon industry, the bit-response to changes in parameters such as drill-bit rotational speed and weight-on-bit (WOB) while drilling into a hydrocarbon reservoir is very much affected by changes in the lithological environment of drilling operations. Accurate and real-time knowledge of a transition from one environment to another, e.g., one formation to another, and real-time analysis of how such environmental conditions impact the effect that parameter changes are likely to have on bit-response may greatly improve the expected rate of penetration (ROP).
Similarly, the constraints that limit the range of the drilling parameters may change as the drilling environment changes. These constraints, e.g., the rate at which cuttings are removed by the drilling fluids, may limit the maximum permissible drilling parameter values. Without accurate knowledge of these changes in the constraints, a driller may not be fully aware of where the constraints lie with respect to the ideal parameter settings and for the sake of erring on the side of caution, which is natural considering the dire consequences of drilling equipment failures and drilling accidents, a driller may operate the drilling process at parameters far removed the actual optimal parameters. Considering that drilling, like many other processes associated with the production and transport of hydrocarbons is an extremely costly procedure, the operation of the drilling system at less than optimal parameters can be extremely costly.
Similarly, accurate measurement of the direction (Toolface) and curvature (Dogleg-Severity (DLS)) of a borehole is necessary for a driller to accurately direct a drilling process to a target. Measurements of these properties are typically taken at rather infrequent intervals (e.g., every 30 to 90 feet) while the drill-bit is off bottom and the drill string is stationary. However, modern drilling equipment may provide for taking directional measurements continuously while drilling. Unfortunately, the measurements obtained while-drilling are generally very noisy and difficult for a driller to interpret because of the noise in the data.
Furthermore, the noise in the data tends to be amplified in any direct computation of the Dogleg-Severity and Toolface from the continuous surveys and the results are generally of such low quality to be of little value to the drillers. As a result, the while-drilling data is often not used in computation of Dogleg-Severity, Toolface and/or the like and instead the infrequent measurements, which require the drilling process to be halted while the measurements are taken, are often still used to determine drilling trajectory and/or the like.
In the hydrocarbon industry, as in other industries, event detection systems have generally depended upon people, such as drilling personnel, to manage processes and to identify occurrences of events, such as a change in a rig state. Examples of rig state detection in drilling may be found in the following references: “The MDS System: Computers Transform Drilling”, Bourgois, Burgess, Rike, Unsworth, Oilfield Review Vol. 2, No. 1, 1990, pp. 4-15; and “Managing Drilling Risk” Aldred et al., Oilfield Review, Summer 1999, pp. 219.
With regard to the hydrocarbon industry, some very limited techniques have been used for detecting a certain type of event, i.e., possible rig states, such as “in slips”, “not in slips”, “tripping in” or “tripping out”. These systems take a small set of rig states, where each rig state is an intentional drilling state, and use probability analysis to retroactively determine which of the set of intentional drilling states the rig has moved into. Probabilistic rig state detection is described in U.S. Pat. No. 7,128,167, the entirety of which is hereby incorporated by reference for all purposes.
In the hydrocarbon industry, there are ever more and better sensors for sensing data related to the exploration, extraction, production and/or transportation of the hydrocarbons. To better control/automate processes related to the exploration, extraction, production and/or transportation of the hydrocarbons and/or to better process/interpret the data for human controllers/operators of the processes related to the exploration, extraction, production and/or transportation of the hydrocarbons the sensed data associated with the processes must be quickly and effectively handled.